mHealth App Prescription in Australian General Practice: Pre-Post Study
Bibliographic record
Abstract
BACKGROUND: Evidence of effectiveness of mobile health (mHealth) apps as well as their usability as non-drug interventions in primary care are emerging around the globe. OBJECTIVE: This study aimed to explore the feasibility of mHealth app prescription by general practitioners (GPs) and to evaluate the effectiveness of an implementation intervention to increase app prescription. METHODS: A single-group, before-and-after study was conducted in Australian general practice. GPs were given prescription pads for 6 mHealth apps and reported the number of prescriptions dispensed for 4 months. After the reporting of month 2, a 2-minute video of one of the apps was randomly selected and sent to each GP. Data were collected through a prestudy questionnaire, monthly electronic reporting, and end-of-study interviews. The primary outcome was the number of app prescriptions (total, monthly, per GP, and per GP per fortnight). Secondary outcomes included confidence in prescribing apps (0-5 scale), the impact of the intervention video on subsequent prescription numbers, and acceptability of the interventions. RESULTS: Of 40 GPs recruited, 39 commenced, and 36 completed the study. In total, 1324 app prescriptions were dispensed over 4 months. The median number of apps prescribed per GP was 30 (range 6-111 apps). The median number of apps prescribed per GP per fortnight increased from the pre-study level of 1.7 to 4.1. Confidence about prescribing apps doubled from a mean of 2 (not so confident) to 4 (very confident). App videos did not affect subsequent prescription rates substantially. Post-study interviews revealed that the intervention was highly acceptable. CONCLUSIONS: mHealth app prescription in general practice is feasible, and our implementation intervention was effective in increasing app prescription. GPs need more tailored education and training on the value of mHealth apps and knowledge of prescribable apps to be able to successfully change their prescribing habits to include apps. The future of sustainable and scalable app prescription requires a trustworthy electronic app repository of prescribable mHealth apps for GPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".